Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x10e3f70f0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x10edda2e8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.2.0
/Users/brad/.pyenv/versions/anaconda3-4.2.0/envs/tflearn/lib/python3.6/site-packages/ipykernel_launcher.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.
  

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real') 
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')    
    learning_rate = tf.placeholder(tf.float32, (), name="learning_late")

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
alpha = 0.01
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        l1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        l1 = tf.layers.batch_normalization(l1, training=True)
        relu1 = tf.maximum(alpha * l1, l1)
        # 14 x 14 x 64
        
        l2 = tf.layers.conv2d(images, 128, 5, strides=2, padding='same')
        l2 = tf.layers.batch_normalization(l2, training=True)
        relu2 = tf.maximum(alpha * l2, l2)
        # 7 x 7 x 128
                
        l3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        l3 = tf.layers.batch_normalization(l3, training=True)
        relu3 = tf.maximum(alpha * l3, l3)
        # 4 x 4 x 256
        
        flat = tf.reshape(relu3, (-1, 4 * 4 * 256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits    
    
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse=not is_train):
        l1 = tf.layers.dense(z, 7 * 7 * 256)
        l1 = tf.reshape(l1, (-1, 7, 7, 256))
        l1 = tf.layers.batch_normalization(l1, training=is_train)
        # 7 x 7 x 256
        
        l2 = tf.layers.conv2d_transpose(l1, 128, 5, strides=2, padding='same')
        l2 = tf.layers.batch_normalization(l2, training=is_train)
        l2 = tf.nn.relu(l2)
        # 14 x 14 x 128
        
        l3 = tf.layers.conv2d_transpose(l2, 64, 5, strides=2, padding='same')
        l3 = tf.layers.batch_normalization(l3, training=is_train)
        l3 = tf.nn.relu(l3)
        # 28 x 28 x 64        
        
        # Output layer
        logits = tf.layers.conv2d_transpose(l3, out_channel_dim, 5, strides=1, padding='same')
        # 28 x 28 x 3
        
        out = tf.tanh(logits)
        
        return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    smooth = 0.1
    
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    # TODO: Implement Function
    d_loss_real = tf.reduce_mean(
                      tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                              labels=tf.ones_like(d_logits_real) * (1 - smooth)))
    d_loss_fake = tf.reduce_mean(
                      tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                              labels=tf.zeros_like(d_logits_real)))
    d_loss = d_loss_real + d_loss_fake

    g_loss = tf.reduce_mean(
                 tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                         labels=tf.ones_like(d_logits_fake)))    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

        return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    steps = 0
    
    input_real, input_z, _ = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images *= 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})                
                _ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                
                if steps % 100 == 0:
                    train_loss_d = sess.run(d_loss, feed_dict={input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch_i + 1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))    
                    
                    show_generator_output(sess, 16, input_z, data_shape[3], data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [36]:
batch_size = 64
z_dim = 100
learning_rate = 0.005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.6906... Generator Loss: 1.4103
Epoch 1/2... Discriminator Loss: 0.8334... Generator Loss: 1.3711
Epoch 1/2... Discriminator Loss: 0.4451... Generator Loss: 3.8850
Epoch 1/2... Discriminator Loss: 0.3593... Generator Loss: 3.8868
Epoch 1/2... Discriminator Loss: 2.4126... Generator Loss: 4.1744
Epoch 1/2... Discriminator Loss: 1.4113... Generator Loss: 0.5571
Epoch 1/2... Discriminator Loss: 1.6659... Generator Loss: 0.3763
Epoch 1/2... Discriminator Loss: 1.2295... Generator Loss: 0.9916
Epoch 1/2... Discriminator Loss: 1.6219... Generator Loss: 2.1624
Epoch 1/2... Discriminator Loss: 1.4911... Generator Loss: 1.4561
Epoch 1/2... Discriminator Loss: 1.4506... Generator Loss: 1.2726
Epoch 1/2... Discriminator Loss: 1.5543... Generator Loss: 1.6103
Epoch 1/2... Discriminator Loss: 1.4146... Generator Loss: 1.1095
Epoch 1/2... Discriminator Loss: 1.3517... Generator Loss: 1.0326
Epoch 1/2... Discriminator Loss: 1.5168... Generator Loss: 1.4233
Epoch 1/2... Discriminator Loss: 1.5318... Generator Loss: 1.6576
Epoch 1/2... Discriminator Loss: 1.5316... Generator Loss: 1.4847
Epoch 1/2... Discriminator Loss: 1.5602... Generator Loss: 1.2995
Epoch 1/2... Discriminator Loss: 1.4159... Generator Loss: 1.3854
Epoch 1/2... Discriminator Loss: 1.2987... Generator Loss: 0.9365
Epoch 1/2... Discriminator Loss: 1.6765... Generator Loss: 0.3420
Epoch 1/2... Discriminator Loss: 1.3916... Generator Loss: 0.8782
Epoch 1/2... Discriminator Loss: 1.3770... Generator Loss: 0.6184
Epoch 1/2... Discriminator Loss: 1.5573... Generator Loss: 0.4080
Epoch 1/2... Discriminator Loss: 1.3859... Generator Loss: 0.5499
Epoch 1/2... Discriminator Loss: 1.4442... Generator Loss: 0.5013
Epoch 1/2... Discriminator Loss: 1.4781... Generator Loss: 0.4989
Epoch 1/2... Discriminator Loss: 1.3975... Generator Loss: 0.6124
Epoch 1/2... Discriminator Loss: 1.5144... Generator Loss: 0.4653
Epoch 1/2... Discriminator Loss: 1.4846... Generator Loss: 0.4913
Epoch 1/2... Discriminator Loss: 1.3811... Generator Loss: 0.6520
Epoch 1/2... Discriminator Loss: 1.4468... Generator Loss: 0.5207
Epoch 1/2... Discriminator Loss: 1.5157... Generator Loss: 0.4513
Epoch 1/2... Discriminator Loss: 1.3589... Generator Loss: 0.9516
Epoch 1/2... Discriminator Loss: 1.3643... Generator Loss: 1.0842
Epoch 1/2... Discriminator Loss: 1.4959... Generator Loss: 1.2208
Epoch 1/2... Discriminator Loss: 1.4029... Generator Loss: 0.6024
Epoch 1/2... Discriminator Loss: 1.3524... Generator Loss: 0.7515
Epoch 1/2... Discriminator Loss: 1.4165... Generator Loss: 1.2729
Epoch 1/2... Discriminator Loss: 1.5846... Generator Loss: 1.4574
Epoch 1/2... Discriminator Loss: 1.3205... Generator Loss: 0.7594
Epoch 1/2... Discriminator Loss: 1.3351... Generator Loss: 0.7890
Epoch 1/2... Discriminator Loss: 1.3442... Generator Loss: 0.9415
Epoch 1/2... Discriminator Loss: 1.4123... Generator Loss: 1.1932
Epoch 1/2... Discriminator Loss: 1.4132... Generator Loss: 1.2645
Epoch 1/2... Discriminator Loss: 1.4899... Generator Loss: 1.3645
Epoch 1/2... Discriminator Loss: 1.4189... Generator Loss: 1.0690
Epoch 1/2... Discriminator Loss: 1.3987... Generator Loss: 1.1041
Epoch 1/2... Discriminator Loss: 1.4939... Generator Loss: 1.2970
Epoch 1/2... Discriminator Loss: 1.3855... Generator Loss: 0.5894
Epoch 1/2... Discriminator Loss: 1.3794... Generator Loss: 0.5818
Epoch 1/2... Discriminator Loss: 1.3364... Generator Loss: 0.7582
Epoch 1/2... Discriminator Loss: 1.4152... Generator Loss: 0.5587
Epoch 1/2... Discriminator Loss: 1.3962... Generator Loss: 0.5593
Epoch 1/2... Discriminator Loss: 1.3065... Generator Loss: 0.7756
Epoch 1/2... Discriminator Loss: 1.3181... Generator Loss: 0.7635
Epoch 1/2... Discriminator Loss: 1.3869... Generator Loss: 0.5995
Epoch 1/2... Discriminator Loss: 1.3491... Generator Loss: 1.0138
Epoch 1/2... Discriminator Loss: 1.2924... Generator Loss: 1.0212
Epoch 1/2... Discriminator Loss: 1.3243... Generator Loss: 0.6985
Epoch 1/2... Discriminator Loss: 1.3957... Generator Loss: 0.5598
Epoch 1/2... Discriminator Loss: 1.3290... Generator Loss: 0.6360
Epoch 1/2... Discriminator Loss: 1.2739... Generator Loss: 0.8157
Epoch 1/2... Discriminator Loss: 1.2359... Generator Loss: 1.0013
Epoch 1/2... Discriminator Loss: 1.5715... Generator Loss: 1.5200
Epoch 1/2... Discriminator Loss: 1.3916... Generator Loss: 0.5658
Epoch 1/2... Discriminator Loss: 1.3507... Generator Loss: 0.6255
Epoch 1/2... Discriminator Loss: 1.2390... Generator Loss: 0.7999
Epoch 1/2... Discriminator Loss: 1.2980... Generator Loss: 1.0417
Epoch 1/2... Discriminator Loss: 1.5339... Generator Loss: 1.7254
Epoch 1/2... Discriminator Loss: 1.2504... Generator Loss: 0.6990
Epoch 1/2... Discriminator Loss: 1.3052... Generator Loss: 0.8208
Epoch 1/2... Discriminator Loss: 1.2049... Generator Loss: 0.9604
Epoch 1/2... Discriminator Loss: 1.3330... Generator Loss: 0.5662
Epoch 1/2... Discriminator Loss: 1.2993... Generator Loss: 0.7292
Epoch 1/2... Discriminator Loss: 1.2515... Generator Loss: 0.6882
Epoch 1/2... Discriminator Loss: 1.2691... Generator Loss: 0.8926
Epoch 1/2... Discriminator Loss: 1.4009... Generator Loss: 1.1591
Epoch 1/2... Discriminator Loss: 1.3449... Generator Loss: 1.1831
Epoch 1/2... Discriminator Loss: 1.1886... Generator Loss: 0.9441
Epoch 1/2... Discriminator Loss: 1.3858... Generator Loss: 0.5042
Epoch 1/2... Discriminator Loss: 1.2354... Generator Loss: 0.9990
Epoch 1/2... Discriminator Loss: 1.4936... Generator Loss: 0.4515
Epoch 1/2... Discriminator Loss: 1.3469... Generator Loss: 1.0696
Epoch 1/2... Discriminator Loss: 1.3459... Generator Loss: 1.0133
Epoch 1/2... Discriminator Loss: 2.0361... Generator Loss: 0.3079
Epoch 1/2... Discriminator Loss: 1.2874... Generator Loss: 0.9881
Epoch 1/2... Discriminator Loss: 1.3328... Generator Loss: 1.0631
Epoch 1/2... Discriminator Loss: 1.3206... Generator Loss: 1.3335
Epoch 1/2... Discriminator Loss: 1.3173... Generator Loss: 1.0180
Epoch 1/2... Discriminator Loss: 1.2450... Generator Loss: 0.8641
Epoch 1/2... Discriminator Loss: 1.3455... Generator Loss: 1.3666
Epoch 1/2... Discriminator Loss: 1.5624... Generator Loss: 0.4083
Epoch 2/2... Discriminator Loss: 1.3074... Generator Loss: 0.6830
Epoch 2/2... Discriminator Loss: 1.1956... Generator Loss: 0.8418
Epoch 2/2... Discriminator Loss: 1.2629... Generator Loss: 0.6683
Epoch 2/2... Discriminator Loss: 1.9150... Generator Loss: 2.2032
Epoch 2/2... Discriminator Loss: 1.2724... Generator Loss: 0.9334
Epoch 2/2... Discriminator Loss: 1.3419... Generator Loss: 1.3637
Epoch 2/2... Discriminator Loss: 1.1478... Generator Loss: 1.0293
Epoch 2/2... Discriminator Loss: 1.7820... Generator Loss: 0.3322
Epoch 2/2... Discriminator Loss: 1.2101... Generator Loss: 0.8599
Epoch 2/2... Discriminator Loss: 1.1485... Generator Loss: 0.8183
Epoch 2/2... Discriminator Loss: 1.2819... Generator Loss: 0.9552
Epoch 2/2... Discriminator Loss: 1.2927... Generator Loss: 1.1717
Epoch 2/2... Discriminator Loss: 1.1482... Generator Loss: 0.7980
Epoch 2/2... Discriminator Loss: 1.1783... Generator Loss: 1.1046
Epoch 2/2... Discriminator Loss: 1.2929... Generator Loss: 0.6339
Epoch 2/2... Discriminator Loss: 1.2460... Generator Loss: 1.2375
Epoch 2/2... Discriminator Loss: 1.2777... Generator Loss: 1.1998
Epoch 2/2... Discriminator Loss: 1.3019... Generator Loss: 0.6070
Epoch 2/2... Discriminator Loss: 1.2140... Generator Loss: 0.7042
Epoch 2/2... Discriminator Loss: 1.2831... Generator Loss: 0.7036
Epoch 2/2... Discriminator Loss: 1.1014... Generator Loss: 0.9299
Epoch 2/2... Discriminator Loss: 1.2374... Generator Loss: 0.6658
Epoch 2/2... Discriminator Loss: 1.5330... Generator Loss: 0.4390
Epoch 2/2... Discriminator Loss: 1.2716... Generator Loss: 1.0006
Epoch 2/2... Discriminator Loss: 1.1885... Generator Loss: 0.9783
Epoch 2/2... Discriminator Loss: 1.2717... Generator Loss: 0.7675
Epoch 2/2... Discriminator Loss: 1.3809... Generator Loss: 0.4977
Epoch 2/2... Discriminator Loss: 1.3133... Generator Loss: 0.6606
Epoch 2/2... Discriminator Loss: 1.3847... Generator Loss: 0.9830
Epoch 2/2... Discriminator Loss: 1.2445... Generator Loss: 0.9937
Epoch 2/2... Discriminator Loss: 1.1726... Generator Loss: 0.9644
Epoch 2/2... Discriminator Loss: 1.4298... Generator Loss: 0.5048
Epoch 2/2... Discriminator Loss: 1.4731... Generator Loss: 1.6295
Epoch 2/2... Discriminator Loss: 1.1464... Generator Loss: 0.9621
Epoch 2/2... Discriminator Loss: 1.3186... Generator Loss: 0.6286
Epoch 2/2... Discriminator Loss: 1.3205... Generator Loss: 0.5515
Epoch 2/2... Discriminator Loss: 1.2283... Generator Loss: 0.8484
Epoch 2/2... Discriminator Loss: 1.1253... Generator Loss: 1.1267
Epoch 2/2... Discriminator Loss: 1.2430... Generator Loss: 0.8943
Epoch 2/2... Discriminator Loss: 1.5312... Generator Loss: 1.1572
Epoch 2/2... Discriminator Loss: 1.2160... Generator Loss: 1.2104
Epoch 2/2... Discriminator Loss: 1.2006... Generator Loss: 0.7393
Epoch 2/2... Discriminator Loss: 1.2703... Generator Loss: 0.6813
Epoch 2/2... Discriminator Loss: 1.3418... Generator Loss: 0.5607
Epoch 2/2... Discriminator Loss: 1.1948... Generator Loss: 1.0710
Epoch 2/2... Discriminator Loss: 1.2300... Generator Loss: 1.4386
Epoch 2/2... Discriminator Loss: 1.6885... Generator Loss: 1.8294
Epoch 2/2... Discriminator Loss: 1.4240... Generator Loss: 0.5109
Epoch 2/2... Discriminator Loss: 1.2166... Generator Loss: 1.2478
Epoch 2/2... Discriminator Loss: 1.2568... Generator Loss: 0.7407
Epoch 2/2... Discriminator Loss: 1.1325... Generator Loss: 1.0247
Epoch 2/2... Discriminator Loss: 1.2166... Generator Loss: 1.1210
Epoch 2/2... Discriminator Loss: 1.1951... Generator Loss: 0.8355
Epoch 2/2... Discriminator Loss: 1.0971... Generator Loss: 0.7942
Epoch 2/2... Discriminator Loss: 1.1479... Generator Loss: 0.9262
Epoch 2/2... Discriminator Loss: 1.2287... Generator Loss: 0.7589
Epoch 2/2... Discriminator Loss: 1.1574... Generator Loss: 1.0918
Epoch 2/2... Discriminator Loss: 1.2974... Generator Loss: 0.6855
Epoch 2/2... Discriminator Loss: 1.3377... Generator Loss: 0.5175
Epoch 2/2... Discriminator Loss: 1.0815... Generator Loss: 1.3344
Epoch 2/2... Discriminator Loss: 1.1322... Generator Loss: 1.4481
Epoch 2/2... Discriminator Loss: 1.3493... Generator Loss: 0.7042
Epoch 2/2... Discriminator Loss: 1.1920... Generator Loss: 1.0186
Epoch 2/2... Discriminator Loss: 1.2120... Generator Loss: 1.0113
Epoch 2/2... Discriminator Loss: 1.2747... Generator Loss: 0.5931
Epoch 2/2... Discriminator Loss: 1.3417... Generator Loss: 0.6001
Epoch 2/2... Discriminator Loss: 1.1425... Generator Loss: 1.1319
Epoch 2/2... Discriminator Loss: 1.3933... Generator Loss: 0.5135
Epoch 2/2... Discriminator Loss: 1.3448... Generator Loss: 1.8945
Epoch 2/2... Discriminator Loss: 1.2336... Generator Loss: 0.6650
Epoch 2/2... Discriminator Loss: 1.2068... Generator Loss: 0.7378
Epoch 2/2... Discriminator Loss: 1.1997... Generator Loss: 1.4284
Epoch 2/2... Discriminator Loss: 1.1648... Generator Loss: 1.4905
Epoch 2/2... Discriminator Loss: 1.2676... Generator Loss: 0.7107
Epoch 2/2... Discriminator Loss: 1.1889... Generator Loss: 1.6262
Epoch 2/2... Discriminator Loss: 1.1226... Generator Loss: 0.7650
Epoch 2/2... Discriminator Loss: 1.2190... Generator Loss: 0.8088
Epoch 2/2... Discriminator Loss: 1.8380... Generator Loss: 0.3288
Epoch 2/2... Discriminator Loss: 1.2734... Generator Loss: 0.7210
Epoch 2/2... Discriminator Loss: 1.5274... Generator Loss: 0.4145
Epoch 2/2... Discriminator Loss: 1.1453... Generator Loss: 1.0405
Epoch 2/2... Discriminator Loss: 1.3376... Generator Loss: 1.8256
Epoch 2/2... Discriminator Loss: 1.2303... Generator Loss: 0.6436
Epoch 2/2... Discriminator Loss: 1.1810... Generator Loss: 1.2504
Epoch 2/2... Discriminator Loss: 1.2225... Generator Loss: 0.7322
Epoch 2/2... Discriminator Loss: 1.0838... Generator Loss: 0.7630
Epoch 2/2... Discriminator Loss: 1.9805... Generator Loss: 1.8441
Epoch 2/2... Discriminator Loss: 1.2748... Generator Loss: 1.2357
Epoch 2/2... Discriminator Loss: 1.2380... Generator Loss: 0.7872
Epoch 2/2... Discriminator Loss: 1.3533... Generator Loss: 0.5291
Epoch 2/2... Discriminator Loss: 1.2194... Generator Loss: 0.7001
Epoch 2/2... Discriminator Loss: 1.2447... Generator Loss: 1.2607
Epoch 2/2... Discriminator Loss: 1.0852... Generator Loss: 1.0929
Epoch 2/2... Discriminator Loss: 1.0991... Generator Loss: 1.0745

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [12]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.5560... Generator Loss: 2.0266
Epoch 1/1... Discriminator Loss: 1.2864... Generator Loss: 0.9656
Epoch 1/1... Discriminator Loss: 1.2960... Generator Loss: 1.2002
Epoch 1/1... Discriminator Loss: 1.4416... Generator Loss: 0.7517
Epoch 1/1... Discriminator Loss: 1.5198... Generator Loss: 0.9827
Epoch 1/1... Discriminator Loss: 1.3618... Generator Loss: 0.6719
Epoch 1/1... Discriminator Loss: 1.3191... Generator Loss: 0.8627
Epoch 1/1... Discriminator Loss: 1.2965... Generator Loss: 0.8796
Epoch 1/1... Discriminator Loss: 1.3947... Generator Loss: 0.7773
Epoch 1/1... Discriminator Loss: 1.3836... Generator Loss: 0.7704
Epoch 1/1... Discriminator Loss: 1.3270... Generator Loss: 0.8666
Epoch 1/1... Discriminator Loss: 1.3498... Generator Loss: 0.7457
Epoch 1/1... Discriminator Loss: 1.3805... Generator Loss: 0.7497
Epoch 1/1... Discriminator Loss: 1.3954... Generator Loss: 1.0006
Epoch 1/1... Discriminator Loss: 1.4061... Generator Loss: 0.7136
Epoch 1/1... Discriminator Loss: 1.4034... Generator Loss: 1.0601
Epoch 1/1... Discriminator Loss: 1.3572... Generator Loss: 0.6871
Epoch 1/1... Discriminator Loss: 1.3874... Generator Loss: 0.7497
Epoch 1/1... Discriminator Loss: 1.3673... Generator Loss: 0.7477
Epoch 1/1... Discriminator Loss: 1.3698... Generator Loss: 0.8054
Epoch 1/1... Discriminator Loss: 1.3408... Generator Loss: 0.8394
Epoch 1/1... Discriminator Loss: 1.3553... Generator Loss: 0.7613
Epoch 1/1... Discriminator Loss: 1.3846... Generator Loss: 0.6222
Epoch 1/1... Discriminator Loss: 1.3836... Generator Loss: 0.7228
Epoch 1/1... Discriminator Loss: 1.3541... Generator Loss: 0.8153
Epoch 1/1... Discriminator Loss: 1.3399... Generator Loss: 0.8357
Epoch 1/1... Discriminator Loss: 1.3806... Generator Loss: 0.7449
Epoch 1/1... Discriminator Loss: 1.3894... Generator Loss: 0.8271
Epoch 1/1... Discriminator Loss: 1.3850... Generator Loss: 0.7515
Epoch 1/1... Discriminator Loss: 1.4070... Generator Loss: 0.9159
Epoch 1/1... Discriminator Loss: 1.3793... Generator Loss: 0.7781

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.